A main product in Video Analytics is PIDS (Perimeter Intrusion Detection System). Normally it includes one fixed video camera, which detects all suspected objects in its field of view (FOV), raises an alarm and tracks the suspected objects until they remain in the FOV.
However, there is a problem in trade-off between the FOV size and the zoom: either the camera sees only narrow region, or the objects are small and not recognizable. PTZ (pan/tilt/zoom) camera comes to solve this trade-off. PTZ camera has 3 degrees of freedom: it may move in two directions (vertical and horizontal) and to zoom-in/out.
There are two types of autonomous PTZ tracking solutions. In the first, the intrusion detection is performed in the PTZ camera (either static or scanning), that continues with tracking after detection. In the second, the intrusion detection is performed in a fixed camera, which triggers the PTZ camera.
The most sensitive part of the PTZ tracking is the object's initial “acquiring” or start of the tracking. Therefore, the existing solutions are less robust especially at this stage. Any moving object that appears in the frame may “catch” the PTZ camera. Even if there are no moving pixels in the frame other than the object, the object's “acquisition” fails frequently because of lack of the clean background model (without the object), especially if it moves toward the camera or goes far from the camera.
All existing human detection algorithms are not exact enough and not fast enough. On one hand, usage of the background model or motion detection as a filter for human detection may reduce the number of false detections to speed up the recognition. On the other hand, we don't have a clean background model. There is an assumption that the human has to move in order to be detected. A moving nuisance in the scene (trees, shadows, etc.), makes the background/motion even less useful. There is a technical need for additional tools for filtering non-relevant candidates of human detection algorithm.